{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入必要的工具包\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "import xgboost as xgb\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from xgboost import plot_importance\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "      <th>price_bathrooms</th>\n",
       "      <th>price_bedrooms</th>\n",
       "      <th>room_diff</th>\n",
       "      <th>room_num</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>...</th>\n",
       "      <th>virtual</th>\n",
       "      <th>walk</th>\n",
       "      <th>walls</th>\n",
       "      <th>war</th>\n",
       "      <th>washer</th>\n",
       "      <th>water</th>\n",
       "      <th>wheelchair</th>\n",
       "      <th>wifi</th>\n",
       "      <th>windows</th>\n",
       "      <th>work</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2950</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2850</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3758</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3300</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4900</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 227 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   bathrooms  bedrooms  price  price_bathrooms  price_bedrooms  room_diff  \\\n",
       "0        1.0         1   2950      1475.000000     1475.000000        0.0   \n",
       "1        1.0         2   2850      1425.000000      950.000000       -1.0   \n",
       "2        1.0         1   3758      1879.000000     1879.000000        0.0   \n",
       "3        1.0         2   3300      1650.000000     1100.000000       -1.0   \n",
       "4        2.0         2   4900      1633.333333     1633.333333        0.0   \n",
       "\n",
       "   room_num  Year  Month  Day  ...   virtual  walk  walls  war  washer  water  \\\n",
       "0       2.0  2016      6   11  ...         0     0      0    0       0      0   \n",
       "1       3.0  2016      6   24  ...         0     0      0    1       0      0   \n",
       "2       2.0  2016      6    3  ...         0     0      0    0       0      0   \n",
       "3       3.0  2016      6   11  ...         0     0      0    0       0      0   \n",
       "4       4.0  2016      4   12  ...         0     0      0    1       0      0   \n",
       "\n",
       "   wheelchair  wifi  windows  work  \n",
       "0           0     0        0     0  \n",
       "1           0     0        0     0  \n",
       "2           0     0        0     0  \n",
       "3           1     0        0     0  \n",
       "4           0     0        0     0  \n",
       "\n",
       "[5 rows x 227 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读取测试的数据\n",
    "test = pd.read_csv(\"RentListingInquries_FE_test.csv\")\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据是经过处理之后的，可直接进行测试准备训练数据，并使用训练好的模型。\n",
    "x_test = test\n",
    "\n",
    "import pickle\n",
    "\n",
    "xgb = pickle.load(open(\"xgb_model.pkl\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#进行测试。并生产测试结果\n",
    "y_test_pred = xgb.predict_proba(x_test)\n",
    "\n",
    "out_df1 = pd.DataFrame(y_test_pred)\n",
    "out_df1.columns = [\"high\", \"medium\", \"low\"]\n",
    "\n",
    "out_df = pd.concat([test,out_df1], axis = 1)\n",
    "out_df.to_csv(\"xgb_Rent.csv\", index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看特征的重要性排序\n",
    "from matplotlib import pyplot\n",
    "from xgboost import plot_importance\n",
    "plot_importance(xgb)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
